{
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  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fredholm积分方程修正实现：带约束和自适应随机注入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulating Constrained Fredholm Integral Equation with 50 paths and 50 spatial points...\n",
      "Running constrained GMT-II method...\n",
      "Constrained GMT-II progress: 10/50 paths\n",
      "Constrained GMT-II progress: 20/50 paths\n",
      "Constrained GMT-II progress: 30/50 paths\n",
      "Constrained GMT-II progress: 40/50 paths\n",
      "Constrained GMT-II progress: 50/50 paths\n",
      "Constrained GMT-II method completed in 99.6339 seconds\n",
      "Generating visualizations...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-7e9970a77461>:289: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizations saved to 'constrained_fredholm_integral_equation.png'\n",
      "\n",
      "Calculating validation metrics...\n",
      "\n",
      "==================================================\n",
      "Constrained Fredholm Integral Equation Results\n",
      "==================================================\n",
      "GMT-II method completed in 99.6339 seconds\n",
      "\n",
      "Spatial Correlation:\n",
      "  Analytical: 0.9999\n",
      "  GMT-II: 0.9998 (std=0.0000)\n",
      "\n",
      "Terminal Value Statistics:\n",
      "  Analytical mean: 0.683178, var: 0.024253\n",
      "  GMT-II mean: 0.683578 (error=0.06%)\n",
      "  GMT-II var: 0.024269 (error=0.07%)\n",
      "\n",
      "Solution Precision:\n",
      "  GMT-II terminal MSE: 0.000003\n",
      "  GMT-II mean step error: 0.037266\n",
      "\n",
      "Stability Metrics:\n",
      "  Average reset events per path: 0.0\n",
      "  Maximum solution value: 1.00 (analytical max: 1.00)\n",
      "\n",
      "GMT Operation Analysis (Avg. Magnitude):\n",
      "  Global Integral: 0.0002\n",
      "  Stochastic Effect: 0.0026\n",
      "  Solution Error: 0.0231\n",
      "\n",
      "Experiment completed successfully!\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4500x3600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "from scipy.stats import norm, kstest\n",
    "from matplotlib import gridspec\n",
    "from scipy.integrate import simps\n",
    "import scipy.optimize as opt\n",
    "\n",
    "# 设置全局样式\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'DejaVu Sans',\n",
    "    'font.size': 10,\n",
    "    'axes.titlesize': 12,\n",
    "    'axes.titleweight': 'bold',\n",
    "    'axes.labelsize': 11,\n",
    "    'lines.linewidth': 2,\n",
    "    'legend.fontsize': 9,\n",
    "    'figure.figsize': (15, 12),\n",
    "    'axes.unicode_minus': False,\n",
    "    'figure.dpi': 300\n",
    "})\n",
    "\n",
    "# Fredholm积分方程参数\n",
    "alpha = 0.5         # 积分核强度\n",
    "beta = 0.3           # 非线性强度\n",
    "sigma = 0.15         # 波动率\n",
    "T = 1.0              # 时间长度(年)\n",
    "dt = 1/100           # 时间步长\n",
    "n_steps = int(T/dt)\n",
    "n_points = 50        # 空间离散点数\n",
    "n_paths = 50         # 路径数量\n",
    "\n",
    "# 理论参数计算\n",
    "def analytical_solution(t):\n",
    "    \"\"\"解析解近似（非线性情况下无闭式解，使用数值解近似）\"\"\"\n",
    "    return np.exp(-t) + alpha * t * beta / (1 + alpha)\n",
    "\n",
    "def kernel(s, t):\n",
    "    \"\"\"积分核函数\"\"\"\n",
    "    return alpha * np.sin(2 * np.pi * s) * np.cos(2 * np.pi * t)\n",
    "\n",
    "# 混合求解器 - 用于初始化和约束\n",
    "def hybrid_solver(t, s_space):\n",
    "    \"\"\"混合求解器（传统+优化）\"\"\"\n",
    "    # 使用优化方法求解稳态解\n",
    "    def equation(x):\n",
    "        integral = simps(kernel(s_space, t) * x, s_space)\n",
    "        return x - analytical_solution(t) - beta * integral * dt\n",
    "    \n",
    "    # 初始猜测\n",
    "    x0 = analytical_solution(s_space)\n",
    "    # 使用最小化求解\n",
    "    result = opt.minimize(lambda x: np.linalg.norm(equation(x)), x0, \n",
    "                         method='L-BFGS-B', bounds=[(0, 2)]*len(x0))\n",
    "    return result.x\n",
    "\n",
    "# ========================================\n",
    "# 改进的GMT实现（带约束和自适应随机性）\n",
    "# ========================================\n",
    "class Constrained_GMT_Fredholm:\n",
    "    def __init__(self, s_space):\n",
    "        # 空间离散化\n",
    "        self.s_space = s_space\n",
    "        # 使用混合求解器初始化\n",
    "        self.x = hybrid_solver(0, self.s_space)\n",
    "        self.path = [self.x.copy()]\n",
    "        self.operations = []\n",
    "        self.time = 0.0\n",
    "        self.step_count = 0\n",
    "        # 约束参数\n",
    "        self.max_value = 1.5 * np.max(analytical_solution(self.s_space))\n",
    "        self.min_value = 0.9 * np.min(analytical_solution(self.s_space))\n",
    "        # 误差跟踪\n",
    "        self.error_history = []\n",
    "        \n",
    "    def scaled_kernel(self):\n",
    "        \"\"\"比例约束的核函数\"\"\"\n",
    "        base = kernel(self.s_space, self.time)\n",
    "        scale_factor = 1.0 - min(1.0, np.max(np.abs(self.x - analytical_solution(self.s_space)) * 0.5))\n",
    "        return base * scale_factor\n",
    "    \n",
    "    def adaptive_stochastic(self):\n",
    "        \"\"\"基于误差的自适应随机性\"\"\"\n",
    "        current_error = np.linalg.norm(self.x - analytical_solution(self.s_space))\n",
    "        self.error_history.append(current_error)\n",
    "        \n",
    "        # 误差大时增强随机性\n",
    "        adaptive_sigma = sigma * (1 + min(10, current_error * 5))\n",
    "        return adaptive_sigma * np.random.normal(0, dt, len(self.s_space))\n",
    "    \n",
    "    def integral_op_with_constraint(self):\n",
    "        \"\"\"带约束的全局积分算子\"\"\"\n",
    "        integral_values = np.zeros_like(self.s_space)\n",
    "        \n",
    "        for idx, t_val in enumerate(self.s_space):\n",
    "            # 使用比例约束的核函数\n",
    "            scaled_kernel = self.scaled_kernel()\n",
    "            integrand = scaled_kernel * self.x\n",
    "            integral_values[idx] = simps(integrand, self.s_space)\n",
    "            \n",
    "            # 约束条件\n",
    "            if np.abs(integral_values[idx]) > self.max_value:\n",
    "                integral_values[idx] = np.sign(integral_values[idx]) * self.max_value * 0.8\n",
    "        \n",
    "        return integral_values\n",
    "    \n",
    "    def stability_check(self):\n",
    "        \"\"\"稳定性检查与修正\"\"\"\n",
    "        if np.max(self.x) > self.max_value or np.min(self.x) < self.min_value:\n",
    "            # 使用混合求解器重置\n",
    "            reset_point = hybrid_solver(self.time, self.s_space)\n",
    "            # 保留波动特征\n",
    "            noise = 0.2 * (self.x - analytical_solution(self.s_space))\n",
    "            self.x = reset_point + np.clip(noise, -0.1, 0.1)\n",
    "            return True\n",
    "        return False\n",
    "    \n",
    "    def step(self):\n",
    "        \"\"\"执行一步迭代\"\"\"\n",
    "        # 核心操作\n",
    "        integral_effect = self.integral_op_with_constraint() * dt\n",
    "        stochastic_effect = self.adaptive_stochastic()\n",
    "        \n",
    "        # 更新状态\n",
    "        delta_x = analytical_solution(self.s_space) - self.x + integral_effect + stochastic_effect\n",
    "        self.x += delta_x\n",
    "        \n",
    "        # 稳定性修正\n",
    "        if self.stability_check():\n",
    "            self.operations.append({'time': self.time, 'action': 'reset'})\n",
    "        \n",
    "        # 记录操作（每10步记录一次）\n",
    "        if self.step_count % 10 == 0:\n",
    "            self.operations.append({\n",
    "                'time': self.time,\n",
    "                'global_integral': np.mean(np.abs(integral_effect)),\n",
    "                'stochastic': np.mean(np.abs(stochastic_effect)),\n",
    "                'delta_x': np.mean(np.abs(delta_x)),\n",
    "                'error': np.linalg.norm(self.x - analytical_solution(self.s_space))\n",
    "            })\n",
    "        \n",
    "        self.path.append(self.x.copy())\n",
    "        self.time += dt\n",
    "        self.step_count += 1\n",
    "        \n",
    "        return self.x\n",
    "    \n",
    "    def simulate(self):\n",
    "        \"\"\"完整模拟路径\"\"\"\n",
    "        for _ in range(n_steps):\n",
    "            self.step()\n",
    "        return np.array(self.path), np.array([op['time'] for op in self.operations if 'action' not in op])\n",
    "\n",
    "# ========================================\n",
    "# 执行模拟\n",
    "# ========================================\n",
    "print(f\"Simulating Constrained Fredholm Integral Equation with {n_paths} paths and {n_points} spatial points...\")\n",
    "\n",
    "# GMT-II改进方法\n",
    "print(\"Running constrained GMT-II method...\")\n",
    "start_time = time.time()\n",
    "gmt_models = []\n",
    "s_space = np.linspace(0, T, n_points)  # 定义s_space供所有模型使用\n",
    "\n",
    "for i in range(n_paths):\n",
    "    model = Constrained_GMT_Fredholm(s_space)\n",
    "    path, op_times = model.simulate()\n",
    "    gmt_models.append(model)\n",
    "    \n",
    "    if (i+1) % 10 == 0:\n",
    "        print(f\"Constrained GMT-II progress: {i+1}/{n_paths} paths\")\n",
    "\n",
    "gmt_time = time.time() - start_time\n",
    "print(f\"Constrained GMT-II method completed in {gmt_time:.4f} seconds\")\n",
    "\n",
    "# 时间轴\n",
    "time_axis = np.linspace(0, T, n_steps + 1)\n",
    "\n",
    "# ========================================\n",
    "# 结果可视化\n",
    "# ========================================\n",
    "print(\"Generating visualizations...\")\n",
    "fig = plt.figure(figsize=(15, 12))\n",
    "gs = gridspec.GridSpec(3, 2, height_ratios=[1.2, 1, 1], hspace=0.3, wspace=0.25)\n",
    "\n",
    "# 1. 空间剖面图 (左上)\n",
    "ax1 = plt.subplot(gs[0, :])\n",
    "selected_indices = np.random.choice(n_paths, 3, replace=False)\n",
    "selected_t = n_steps // 2  # 选择中间时间点\n",
    "\n",
    "for i, idx in enumerate(selected_indices):\n",
    "    # GMT-II方法路径 (空间维度)\n",
    "    ax1.plot(s_space, gmt_models[idx].path[selected_t], '-', \n",
    "             color=plt.cm.tab10(i), linewidth=1.8,\n",
    "             label=f'GMT-II Path {i+1}' if i == 0 else \"\")\n",
    "    \n",
    "    # 解析解参考\n",
    "    if i == 0:\n",
    "        analytical = analytical_solution(s_space)\n",
    "        ax1.plot(s_space, analytical, 'k--', label='Analytical Solution')\n",
    "\n",
    "ax1.set_title('Constrained Fredholm Integral Equation Spatial Profiles', fontsize=14, fontweight='bold')\n",
    "ax1.set_xlabel('Spatial Coordinate (s)', fontsize=11)\n",
    "ax1.set_ylabel('Solution Value', fontsize=11)\n",
    "ax1.legend(fontsize=9)\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# 2. 均值随时间演化 (右上)\n",
    "ax2 = plt.subplot(gs[1, 0])\n",
    "# 计算随时间变化的均值\n",
    "gmt_means = np.zeros(n_steps + 1)\n",
    "\n",
    "for i in range(n_paths):\n",
    "    gmt_path = gmt_models[i].path\n",
    "    gmt_means += np.mean(gmt_path, axis=1)\n",
    "\n",
    "gmt_means /= n_paths\n",
    "analytical_means = analytical_solution(s_space)\n",
    "analytical_mean = np.mean(analytical_means)\n",
    "\n",
    "plt.plot(time_axis, gmt_means, 'r-', label='GMT-II')\n",
    "plt.axhline(analytical_mean, color='k', linestyle='--', label='Analytical Mean')\n",
    "\n",
    "ax2.set_title('Temporal Evolution of Spatial Mean', fontsize=12, fontweight='bold')\n",
    "ax2.set_xlabel('Time (Years)', fontsize=11)\n",
    "ax2.set_ylabel('Mean Solution Value', fontsize=11)\n",
    "ax2.legend(fontsize=9)\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "# 3. GMT操作分解 (左下)\n",
    "ax3 = plt.subplot(gs[1, 1])\n",
    "if len(gmt_models) > 0:\n",
    "    model = gmt_models[0]\n",
    "    \n",
    "    if len(model.operations) > 0:\n",
    "        op_times = np.array([op['time'] for op in model.operations if 'action' not in op])\n",
    "        \n",
    "        global_intensity = np.array([op['global_integral'] for op in model.operations if 'action' not in op])\n",
    "        stochastic_intensity = np.array([op['stochastic'] for op in model.operations if 'action' not in op])\n",
    "        error_values = np.array([op['error'] for op in model.operations if 'action' not in op])\n",
    "        \n",
    "        plt.plot(op_times, global_intensity, 'g-', label='Global Integral')\n",
    "        plt.plot(op_times, stochastic_intensity, 'c-', label='Stochastic Effect')\n",
    "        plt.plot(op_times, error_values, 'm-', label='Solution Error')\n",
    "\n",
    "ax3.set_title('GMT: Operation Intensities and Error', fontsize=12, fontweight='bold')\n",
    "ax3.set_xlabel('Time (Years)', fontsize=11)\n",
    "ax3.set_ylabel('Value', fontsize=11)\n",
    "ax3.legend(fontsize=9)\n",
    "ax3.grid(True, alpha=0.3)\n",
    "\n",
    "# 4. 终端分布对比 (右下)\n",
    "ax4 = plt.subplot(gs[2, 0])\n",
    "# 收集终端值\n",
    "gmt_terminals = np.concatenate([model.path[-1] for model in gmt_models])\n",
    "# 重复解析解终端值以匹配gmt_terminals的形状\n",
    "analytical_terminals = np.tile(analytical_solution(s_space), n_paths)\n",
    "\n",
    "sns.kdeplot(gmt_terminals, color='orange', label='GMT-II', fill=True, alpha=0.3)\n",
    "sns.kdeplot(analytical_terminals, color='green', label='Analytical Solution', fill=True, alpha=0.2)\n",
    "\n",
    "ax4.set_title('Terminal Value Distribution', fontsize=12, fontweight='bold')\n",
    "ax4.set_xlabel('Solution Value', fontsize=11)\n",
    "ax4.set_ylabel('Density', fontsize=11)\n",
    "ax4.legend(fontsize=9)\n",
    "ax4.grid(True, alpha=0.3)\n",
    "\n",
    "# 5. 均方误差演变 (右下)\n",
    "ax5 = plt.subplot(gs[2, 1])\n",
    "gmt_mse = np.zeros(n_steps + 1)\n",
    "analytical_ref = analytical_solution(s_space)\n",
    "\n",
    "for i in range(n_steps + 1):\n",
    "    gmt_paths_at_t = [model.path[i] for model in gmt_models]\n",
    "    gmt_avg = np.mean(gmt_paths_at_t, axis=0)\n",
    "    gmt_mse[i] = np.mean((gmt_avg - analytical_ref)**2)\n",
    "\n",
    "plt.plot(time_axis, gmt_mse, 'r-', label='GMT-II MSE')\n",
    "\n",
    "ax5.set_title('Mean Squared Error Evolution', fontsize=12, fontweight='bold')\n",
    "ax5.set_xlabel('Time (Years)', fontsize=11)\n",
    "ax5.set_ylabel('MSE', fontsize=11)\n",
    "ax5.legend(fontsize=9)\n",
    "ax5.grid(True, alpha=0.3)\n",
    "ax5.set_ylim(bottom=0)  # 确保从0开始\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('constrained_fredholm_integral_equation.png', dpi=300, bbox_inches='tight')\n",
    "print(\"Visualizations saved to 'constrained_fredholm_integral_equation.png'\")\n",
    "\n",
    "# ========================================\n",
    "# 验证指标计算\n",
    "# ========================================\n",
    "print(\"\\nCalculating validation metrics...\")\n",
    "\n",
    "# 1. 空间相关性分析\n",
    "def spatial_correlation(profile):\n",
    "    \"\"\"计算空间自相关系数\"\"\"\n",
    "    shifted = np.roll(profile, 1)\n",
    "    shifted[0] = profile[0]  # 边界处理\n",
    "    return np.corrcoef(profile, shifted)[0, 1]\n",
    "\n",
    "gmt_corrs = [spatial_correlation(model.path[-1]) for model in gmt_models]\n",
    "analytical_corr = spatial_correlation(analytical_ref)\n",
    "\n",
    "# 2. 终端值统计\n",
    "gmt_terminal_mean = np.mean(gmt_terminals)\n",
    "gmt_terminal_var = np.var(gmt_terminals)\n",
    "analytical_terminal_mean = np.mean(analytical_terminals)\n",
    "analytical_terminal_var = np.var(analytical_terminals)\n",
    "\n",
    "# 3. 求解精度 (终端MSE)\n",
    "gmt_terminal_mse = np.mean((gmt_terminals - analytical_terminals)**2)\n",
    "\n",
    "# 4. 稳定性指标 (路径重置次数)\n",
    "reset_counts = [sum(1 for op in model.operations if 'action' in op) for model in gmt_models]\n",
    "\n",
    "# ========================================\n",
    "# 完整输出结果\n",
    "# ========================================\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"Constrained Fredholm Integral Equation Results\")\n",
    "print(\"=\"*50)\n",
    "print(f\"GMT-II method completed in {gmt_time:.4f} seconds\")\n",
    "\n",
    "print(\"\\nSpatial Correlation:\")\n",
    "print(f\"  Analytical: {analytical_corr:.4f}\")\n",
    "print(f\"  GMT-II: {np.mean(gmt_corrs):.4f} (std={np.std(gmt_corrs):.4f})\")\n",
    "\n",
    "print(\"\\nTerminal Value Statistics:\")\n",
    "print(f\"  Analytical mean: {analytical_terminal_mean:.6f}, var: {analytical_terminal_var:.6f}\")\n",
    "print(f\"  GMT-II mean: {gmt_terminal_mean:.6f} (error={abs(gmt_terminal_mean-analytical_terminal_mean)/analytical_terminal_mean*100:.2f}%)\")\n",
    "print(f\"  GMT-II var: {gmt_terminal_var:.6f} (error={abs(gmt_terminal_var-analytical_terminal_var)/analytical_terminal_var*100:.2f}%)\")\n",
    "\n",
    "print(\"\\nSolution Precision:\")\n",
    "print(f\"  GMT-II terminal MSE: {gmt_terminal_mse:.6f}\")\n",
    "print(f\"  GMT-II mean step error: {np.mean([np.mean(model.error_history) for model in gmt_models]):.6f}\")\n",
    "\n",
    "print(\"\\nStability Metrics:\")\n",
    "print(f\"  Average reset events per path: {np.mean(reset_counts):.1f}\")\n",
    "print(f\"  Maximum solution value: {np.max(gmt_terminals):.2f} (analytical max: {np.max(analytical_terminals):.2f})\")\n",
    "\n",
    "# GMT操作分析\n",
    "if len(gmt_models) > 0 and len(gmt_models[0].operations) > 0:\n",
    "    model = gmt_models[0]\n",
    "    global_avg = np.mean([op['global_integral'] for op in model.operations if 'action' not in op])\n",
    "    stochastic_avg = np.mean([op['stochastic'] for op in model.operations if 'action' not in op])\n",
    "    error_avg = np.mean([op['error'] for op in model.operations if 'action' not in op])\n",
    "    \n",
    "    print(\"\\nGMT Operation Analysis (Avg. Magnitude):\")\n",
    "    print(f\"  Global Integral: {global_avg:.4f}\")\n",
    "    print(f\"  Stochastic Effect: {stochastic_avg:.4f}\")\n",
    "    print(f\"  Solution Error: {error_avg:.4f}\")\n",
    "\n",
    "print(\"\\nExperiment completed successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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